{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:44:35Z","timestamp":1770338675601,"version":"3.49.0"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"22","license":[{"start":{"date-parts":[[2019,4,27]],"date-time":"2019-04-27T00:00:00Z","timestamp":1556323200000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Swedish Cancer Fonden, the Swedish Research Council"},{"DOI":"10.13039\/501100001729","name":"Swedish Foundation for Strategic Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001729","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100003392","name":"SSF","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Both single-cell RNA sequencing (scRNA-seq) and DNA sequencing (scDNA-seq) have been applied for cell-level genomic profiling. For mutation profiling, the latter seems more natural. However, the task is highly challenging due to the limited input materials from only two copies of DNA molecules, while whole-genome amplification generates biases and other technical noises. ScRNA-seq starts with a higher input amount, so generally has better data quality. There exists various methods for mutation detection from DNA sequencing, it is not clear whether these methods work for scRNA-seq data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Mutation detection methods developed for either bulk-cell sequencing data or scDNA-seq data do not work well for the scRNA-seq data, as they produce substantial numbers of false positives. We develop a novel and robust statistical method\u2014called SCmut\u2014to identify specific cells that harbor mutations discovered in bulk-cell data. Statistically SCmut controls the false positives using the 2D local false discovery rate method. We apply SCmut to several scRNA-seq datasets. In scRNA-seq breast cancer datasets SCmut identifies a number of highly confident cell-level mutations that are recurrent in many cells and consistent in different samples. In a scRNA-seq glioblastoma dataset, we discover a recurrent cell-level mutation in the PDGFRA gene that is highly correlated with a well-known in-frame deletion in the gene. To conclude, this study contributes a novel method to discover cell-level mutation information from scRNA-seq that can facilitate investigation of cell-to-cell heterogeneity.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source codes and bioinformatics pipeline of SCmut are available at https:\/\/github.com\/nghiavtr\/SCmut.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz288","type":"journal-article","created":{"date-parts":[[2019,4,17]],"date-time":"2019-04-17T12:51:47Z","timestamp":1555505507000},"page":"4679-4687","source":"Crossref","is-referenced-by-count":45,"title":["Cell-level somatic mutation detection from single-cell RNA sequencing"],"prefix":"10.1093","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7945-5750","authenticated-orcid":false,"given":"Trung Nghia","family":"Vu","sequence":"first","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Stockholm 17177, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ha-Nam","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Information Technology Institute, Vietnam National University in Hanoi , Hanoi 84024, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefano","family":"Calza","sequence":"additional","affiliation":[{"name":"Department of Molecular and Translational Medicine, University of Brescia , Brescia 25125, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krishna R","family":"Kalari","sequence":"additional","affiliation":[{"name":"Department of Health Sciences Research, Mayo Clinic , Rochester, MN 55905, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liewei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Molecular Pharmacology & Experimental Therapeutics , Mayo Clinic, Rochester, MN 55905, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yudi","family":"Pawitan","sequence":"additional","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Stockholm 17177, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,4,26]]},"reference":[{"key":"2023013108311937200_btz288-B1","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.ajhg.2014.12.001","article-title":"Biased allelic expression in human primary fibroblast single cells","volume":"96","author":"Borel","year":"2015","journal-title":"Am. 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